{"id":"W2089477335","doi":"10.3182/20080706-5-kr-1001.00331","title":"Adaptive Model Predictive Control for Constrained Nonlinear Systems","year":2008,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Model predictive control; Control theory (sociology); Nonlinear system; Adaptive control; Robustness (evolution); Estimator; Identifier; Computer science; Mathematical optimization; System identification; Controller (irrigation); Identification (biology); Mathematics; Control (management); Data modeling; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004530682,0.0006790383,0.0009137099,0.000380879,0.0004718279,0.001030877,0.0008348587,0.0008000794,0.002512123],"category_scores_gemma":[0.002050369,0.0004432295,0.0003115245,0.0006612602,0.0007411852,0.0007925934,0.001096922,0.001071883,0.0002977548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004618288,"about_ca_system_score_gemma":0.0007694213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01432241,"about_ca_topic_score_gemma":0.009494094,"domain_scores_codex":[0.999777,0.00005953895,0.00001021279,0.00003684971,0.0000878587,0.00002854349],"domain_scores_gemma":[0.9995918,0.0002218772,0.00004431614,0.00004141582,0.00008956454,0.00001098077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007535967,0.00003484212,0.0001368656,0.0001122112,0.00003624707,0.00006044559,0.00005489207,0.9051004,0.002090062,0.01489099,0.002903288,0.07450442],"study_design_scores_gemma":[0.000005680105,0.00001066562,0.00006030244,0.000003680297,0.000002927681,0.000003949644,0.000002400714,0.9954119,0.0001573133,0.003863075,0.0004753343,0.000002861021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02413324,0.002379026,0.9623126,0.0006506918,0.0003540499,0.00003843734,0.00007042215,0.0004759174,0.009585548],"genre_scores_gemma":[0.9567623,0.00124849,0.03214508,0.0001429113,0.0002440264,0.0001485261,0.000131503,0.00006842961,0.009108776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01432241,"threshold_uncertainty_score":0.02847809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207988610178388,"score_gpt":0.2019351462602545,"score_spread":0.1898552601584707,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}